2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第10页

Data Driven Modeling Social Media Influence using Differential Equations 使用微分方程的数据驱动社交媒体影响建模

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-07-27 DOI: 10.1109/ASONAM55673.2022.10068693

Bailu Jin, Wei Guo

{"title":"Data Driven Modeling Social Media Influence using Differential Equations","authors":"Bailu Jin, Wei Guo","doi":"10.1109/ASONAM55673.2022.10068693","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068693","url":null,"abstract":"Individuals modify their opinions towards a topic based on their social interactions. Opinion evolution models conceptualize the change of opinion as a uni -dimensional continuum, and the effect of influence is built by the group size, the network structures, or the relations among opinions within the group. However, how to model the personal opinion evolution process under the effect of the online social influence as a function remains unclear. Here, we show that the uni -dimensional continuous user opinions can be represented by compressed high-dimensional word embeddings, and its evolution can be accurately modelled by an ordinary differential equation (ODE) that reflects the social network influencer interactions. We perform our analysis on 87 active users with corresponding influencers on the COVID-19 topic from 2020 to 2022. The regression results demonstrate that 99% of the variation in the quantified opinions can be explained by the way we model the connected opinions from their influencers. Our research on the COVID-19 topic and for the account analysed shows that social media users primarily shift their opinion based on influencers they follow (e.g., model explains for 99% variation) and self-evolution of opinion over a long time scale is limited.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134083914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Extracting and Visualizing Wildlife Trafficking Events from Wildlife Trafficking Reports 从野生动物贩运报告中提取和可视化野生动物贩运事件

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-07-17 DOI: 10.1109/ASONAM55673.2022.10068633

Devin Coughlin, Maylee Gagnon, Victoria Grasso, Guanyi Mou, Kyumin Lee, R. Konrad, P. Raxter, Meredith L. Gore

引用次数: 0

A Fast Algorithm for Ranking Users by their Influence in Online Social Platforms 一种基于网络社交平台影响力的用户快速排序算法

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-06-20 DOI: 10.1109/ASONAM55673.2022.10068673

Nouamane Arhachoui, Esteban Bautista, Maximilien Danisch, A. Giovanidis

{"title":"A Fast Algorithm for Ranking Users by their Influence in Online Social Platforms","authors":"Nouamane Arhachoui, Esteban Bautista, Maximilien Danisch, A. Giovanidis","doi":"10.1109/ASONAM55673.2022.10068673","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068673","url":null,"abstract":"Measuring the influence of users in social networks is key for numerous applications. A recently proposed influence metric, coined as Ψ-score, allows to go beyond traditional centrality metrics, which only assess structural graph importance, by further incorporating the rich information provided by the posting and re-posting activity of users. The Ψ-score is shown in fact to generalize PageRank for non-homogeneous node activity. Despite its significance, it scales poorly to large datasets; for a network of $N$ users, it requires to solve $N$ linear systems of equations of size $N$. To address this problem, this work introduces a novel scalable algorithm for the fast approximation of Ψ- score, named Power-Ψ. The proposed algorithm is based on a novel equation indicating that it suffices to solve one system of equations of size $N$ to compute the Ψ-score. Then, our algorithm exploits the fact that such a system can be recursively and distributedly approximated to any desired error. This permits the Ψ-score, summarizing both structural and behavioral information for the nodes, to run as fast as PageRank. We validate the effectiveness of the proposed algorithm, which we release as an open source Python library, on several real-world datasets.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129413215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discovering Affinity Relationships between Personality Types 发现人格类型之间的亲和关系

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-02-21 DOI: 10.1109/ASONAM55673.2022.10068566

Jean Marie Tshimula, B. Chikhaoui, Shengrui Wang

引用次数: 2

Deep Graph Clustering with Random-walk based Scalable Learning 基于随机行走的可扩展学习深度图聚类

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2021-12-31 DOI: 10.1109/ASONAM55673.2022.10068646

Xiang Li, Dong Li, R. Jin, G. Agrawal, R. Ramnath

{"title":"Deep Graph Clustering with Random-walk based Scalable Learning","authors":"Xiang Li, Dong Li, R. Jin, G. Agrawal, R. Ramnath","doi":"10.1109/ASONAM55673.2022.10068646","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068646","url":null,"abstract":"Interactions between (social) entities can be frequently represented by an attributed graph, and node clustering in such graphs has received much attention lately. Multiple efforts have successfully applied Graph Convolutional Networks (GCN), though with some limits on accuracy as GCNs have been shown to suffer from over-smoothing issues. Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability. This paper addresses this open problem by relating the Laplacian smoothing to the Generalized PageRank, and applying a random-walk based algorithm as a scalable graph filter. This forms the basis for our scalable deep clustering algorithm, RwSL. Using 6 real-world datasets and 6 clustering metrics, we show that RwSL achieved improved results over several recent baselines. Most notably, by demonstrating execution of RwSL on a graph with 1.8 billion edges using only a single GPU. We show that RwSL can continue to scale, unlike other existing deep clustering frameworks.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131351120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Predicting Influential Higher-Order Patterns in Temporal Network Data 预测影响时间网络数据的高阶模式

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2021-07-26 DOI: 10.1109/ASONAM55673.2022.10068582

Christoph Gote, Vincenzo Perri, Ingo Scholtes

{"title":"Predicting Influential Higher-Order Patterns in Temporal Network Data","authors":"Christoph Gote, Vincenzo Perri, Ingo Scholtes","doi":"10.1109/ASONAM55673.2022.10068582","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068582","url":null,"abstract":"Networks are frequently used to model complex systems comprised of interacting elements. While edges capture the topology of direct interactions, the true complexity of many systems originates from higher-order patterns in paths by which nodes can indirectly influence each other. Path data, representing ordered sequences of consecutive direct interactions, can be used to model these patterns. On the one hand, to avoid overfitting, such models should only consider those higher-order patterns for which the data provide sufficient statistical evidence. On the other hand, we hypothesise that network models, which capture only direct interactions, underfit higher-order patterns present in data. Consequently, both approaches are likely to misidentify influential nodes in complex networks. We contribute to this issue by proposing five centrality measures based on MOGen, a multi-order generative model that accounts for all indirect influences up to a maximum distance but disregards influences at higher distances. We compare MOGen-based centralities to equivalent measures for network models and path data in a prediction experiment where we aim to identify influential nodes in out-of-sample data. Our results show strong evidence supporting our hypothesis. MOGen consistently outperforms both the network model and path-based prediction. We further show that the performance difference between MOGen and the path-based approach disappears if we have sufficient observations, confirming that the error is due to overfitting.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132591439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection deap - Fake:基于知识图谱的假新闻检测方法

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2021-07-04 DOI: 10.1109/ASONAM55673.2022.10068653

Mohit Mayank, Shakshi Sharma, Rajesh Sharma

{"title":"DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection","authors":"Mohit Mayank, Shakshi Sharma, Rajesh Sharma","doi":"10.1109/ASONAM55673.2022.10068653","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068653","url":null,"abstract":"Fake News on social media platforms has attracted a lot of attention in recent times, primarily for events related to politics (2016 US Presidential elections), and healthcare (infodemic during COVID-19), to name a few. Various methods have been proposed for detecting Fake News. The approaches span from exploiting techniques related to network analysis, Natural Language Processing (NLP), and the usage of Graph Neural Networks (GNNs). In this work, we propose DEAP-FAKED, a knowleDgE grAPh FAKe nEws Detection framework for identifying Fake News. Our approach combines natural language processing (NLP) and tensor decomposition model to encode news content and embed Knowledge Graph (KG) entities, respectively. A variety of these encodings provides a complementary advantage to our detector. We evaluate our framework using two publicly available datasets containing articles from domains such as politics, business, technology, and healthcare. As part of dataset pre-processing, we also remove the bias, such as the source of the articles, which could impact the performance of the models. DEAP-FAKED obtains an F1-score of 88% and 78% for the two datasets, which is an improvement of ~21 %, and ~3%, respectively, which shows the effectiveness of the approach.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130149585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

NodeSig: Binary Node Embeddings via Random Walk Diffusion 节点设计:基于随机游走扩散的二元节点嵌入

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-10-01 DOI: 10.1109/ASONAM55673.2022.10068621

Abdulkadir Çelikkanat, Fragkiskos D. Malliaros, A. Papadopoulos

引用次数: 2